Fine-grained segmentation of high-resolution crack images based on transformer and rendering techniques

High-resolution (HR) imaging is crucial for structural defect detection, yet conventional deep learning models struggle to balance edge segmentation precision with computational efficiency in HR crack image analysis. To address these challenges, a HR Crack Image Rendering Segmentation Network (HRCRSN) is proposed. Three customized improvements were made, enabling the HRCRSN to exploit the advantages of edge-aware rendering technique from the field of computer graphics in the precise segmentation of HR crack images. First, a Transformer-based crack localization module with adaptive multi-scale feature fusion (MSFAWFS) enhances pixel-level guidance while preserving micro-crack details. Second, dynamic point sampling prioritizes ambiguous boundaries and sub-millimeter defects via asymmetric density allocation during training/inference. Third, a synthetic augmentation framework recombines crack objects to address data scarcity. Experiments on UAV-acquired datasets achieve state-of-the-art performance (IoU: 85.36 %, mBA: 92.07 %, DICE: 91.78 %). This approach improves inspection efficiency and establishes a new framework for UAV-based civil infrastructure monitoring.